Encodji: Encoding gaze data into emoji space for an amusing scanpath classification approach

To this day, a variety of information has been obtained from human eye movements, which holds an imense potential to understand and classify cognitive processes and states – e.g., through scanpath classification. In this work, we explore the task of scanpath classification through a combination of unsupervised feature learning and convolutional neural networks. As an amusement factor, we use an Emoji space representation as feature space. This representation is achieved by training generative adversarial networks (GANs) for unpaired scanpath-to-Emoji translation with a cyclic loss. The resulting Emojis are then used to train a convolutional neural network for stimulus prediciton, showing an accuracy improvement of more than five percentual points compared to the same network trained using solely the scanpath data. As a side effect, we also obtain novel unique Emojis representing each unique scanpath. Our goal is to demonstrate the applicability and potential of unsupervised feature learning to scanpath classification in a humorous and entertaining way.